Abstract

Vehicular networks are sets of surface transportation systems that have the ability to communicate with each other. There are several possible network architectures to organize their in-vehicle computing systems. Potential schemes may include vehicle-to-vehicle ad hoc networks, wired backbone with wireless last hops, or hybrid architectures using vehicle-to-vehicle communications to augment roadside communication infrastructures. Some special properties of
these networks, such as high mobility, network partitioning, and constrained topology, differentiate them from other types of wireless networks. We provide an in-depth discussion on the important studies related to architectural design and routing for such networks. Moreover, we discuss the major security concerns appearing in vehicular networks.

1. Introduction

The widespread proliferation of computing systems, during the last decade, has enabled the setup and deployment of vehicular networks in all kinds of environments. The FCC has allocated 75 MHz of spectrum at 5.9 GHz for short-range communications between vehicles and
from vehicles to roadside facilities. Such networks offer the potential for
fast and accurate driving information (e.g., traffic, accidents, and emissions)
that would otherwise be more difficult to disseminate. Hence, new ways to
improve and optimize the transportation system are enabled. Also a variety of
commercial applications can be easily supported. Vehicular networks can be used
to facilitate the service customization to the needs of individual nodes.
Possible applications for such networks can be generally classified as safety and
nonsafety applications. Safety applications include accident avoidance and
cooperative driving [1]. Nonsafety applications include traffic information
[2], toll service,
Internet access [3],
cooperative entertainment, and so on.

Vehicular networks consist of nodes-vehicles equipped
with wireless communication devices, GPS, digital maps, and optional sensors
for reporting the vehicle condition. Vehicles exchange information with other
vehicles as well as with access points (base stations) within their radio
range. Ad hoc or infrastructure wireless networks are used to propagate
information. However, the data propagation requires innovative routing
algorithms. This is because, as we explain later, vehicular networks have
unique characteristics that differentiate them from common wireless networks.
As a consequence, routing is a challenging task due to the high dynamics of
such a network.

In this article, we investigate the following. (1) The extent
to which the vehicular network characteristics can determine the performance of
routing. (2) How can current wireless technologies (e.g., WiFi, UWB, WiMax, and
cellular) support vehicular networks? (3) Most of the important related studies
on vehicular network architectures and routing. We do not focus specifically on
one type of architecture; in contrast we describe and comment on most currently
famous suggested schemes. (4) Finally, how feasible is to maintain a level of security in such networks?

The remainder of this article is organized as follows. In
Section 2, we discuss the unique characteristics of vehicular networks, and how
these demand innovative routing protocols. In Section 3, we present related
studies on vehicular network routing. We also add our comments about the
validity and the importance of them. Some routing and topological security
concerns are mentioned in Section 4. Specifically, we discuss how can the
vehicular network properties affect the impact of malicious attacks, as well as
some general mitigation schemes. Finally, Section 5 concludes this paper.

2. Background

In this
section, we first describe the unique properties of vehicular networks. Next,
we discuss the applicability of current wireless technologies on vehicular
networks. Finally, we discuss potential applications for such networks.

2.1. Vehicular Network Properties

As we mentioned
earlier, vehicular networks have specific characteristics. These properties
affect the decisions that designers have to take when building architectures
for such networks. This is because some of their characteristics prohibit the
use of current routing protocols that are applicable to regular wireless
networks. Here we discuss these uniquenesses.

Geographically Constrained TopologyRoads limit the network topology to actually one
dimension: the road direction. Except for crossroads or overlay bridges, roads
are generally located far apart. Even in urban areas, where they are located
close to each other, there exist obstacles, such as buildings and advertisement
walls, which prevent wireless signals from traveling between roads. This
implies that nodes-vehicles can be considered as points of the same line; a
road can be approximated as a straight line or a small-angled curve. This
observation is quite important, because it affects the wireless technologies
that can be considered. For example, since the packet relays are almost all in
the same one-directional deployment region, the use of directional antennas
could be of great advantage.

Partitioning and Large ScaleIn vehicular networks, the probability of end-to-end
connectivity decreases with distance [4]; this is true for one-dimensional network topologies.
In contrast, connectivity is often explicitly assumed in research for
traditional ad hoc networks, sometimes even for the evaluation of routing
protocols. In addition, vehicular networks can extend in large areas as far as
there is road available. This artifact together with the one-dimensional
deployment increases the above probability.

Predictable MobilityBecause vehicle mobility depends on the deployment
scenario, the movement direction is predictable to some extent. In highways,
vehicles often move at high speeds, while in urban areas they are slow. In
addition, mobility is restricted by the road directions as well as by traffic
regulations. Assuming that these regulations are obeyed, there are lower and
upper speed bounds, and restriction signs that obligate drivers to move on specific
roads and directions. Hence, mobility models can now include some level of
predictability in movement patterns. Car manufacturing companies have already
implemented such models for testing mechanical parts.

Power ConsumptionIn traditional wireless networks, nodes are power limited and their life
depends on their batteries (this is especially the case for ad hoc networks).
Vehicles however can provide continuous power to their computing and
communication devices. As a result, routing protocols do not have to account
for methodologies that try to prolong the battery life. Older network protocols
include mechanisms such as battery-life reports for energy-efficient path
selection, sleep-awake intervals, as well as advanced network/MAC cross-layer
coordination algorithms. These schemes cannot offer any additional advantages
to vehicular networks.

Node ReliabilityVehicles may join and leave the network at any time and much more
frequently than in other wireless networks. The arrival/departure rate depends
on their speed, the environment, as well as on the drivers‘ needs to be
connected to the network. Especially for ad hoc deployments, the network cannot
easily depend on a single vehicle for packet forwarding. This is because the
duration of the vehicle‘s cooperation depends on its destination. Also, apart
from vehicles failing in unpredictable ways, security issues come into play. We
discuss these issues later in this article.

2.2. Potentially Applicable Wireless Technologies

There are three
potential wireless technologies under discussion that can be adopted: wireless
metropolitan area networks (WMANs), wireless local area networks (WLANs/WiFi),
and wireless personal area networks (WPANs), together with their ad hoc mode of operation.

WMANsA WMAN (wireless metropolitan area network) can interconnect distant
locations. Two kinds of WMANs exist: back haul and last mile. Back haul is for
enterprise networks, cellular base station communications, and Wi-Fi hotspots.
A private WMAN broadband system is a quite chip solution and it is 10 times
faster than a DSL or T1 wireline connection. Thus, it is affordable for
companies that do not wish to pay double the price for a fiber 10-Mbps link to
their ISPs. Last-mile setups can establish wireless as an alternative to residential
broadband modems. In a typical cell radius deployment of three to ten
kilometers, last mile systems can be expected to deliver capacity of up to 40 Mbps per channel. This is enough bandwidth to simultaneously support hundreds
of businesses with T1 speed connectivity and thousands of residences with DSL
speed connectivity. WMAN connections can be PTP (point-to-point) or PMP
(point-to-multipoint). Both omnidirectional and directional antennas can be
used, as well as dynamically alternated radio channels and antenna
polarization. PMP setups, where a central point serves multiple remote sites,
are preferable when the density of links is high. PMP systems typically use a
polling protocol to support high-density applications. One of the most interesting
recent developments is the standardization of WMANs in the form of IEEE 802.16.
Finally, the WMAN category also includes the GSM/GPRS cellular infrastructure
networks.The WMAN type of technology could be employed in
infrastructure-based vehicular networks alone, or in coordination with WLANs or
WPANs (and their ad hoc multihop types) as last-hops. WiMax promises to
bring wireless high-speed connections to entire metropolitan areas. It is
currently supported by 140 companies. WiMax has a reach of 1 to 10 miles,
offering a way to bring the Internet to entire communities. Mobile network
deployments are expected to provide up to 15 Mbps of capacity within a typical
cell radius deployment of up to 3 kilometers. This is an obviously
high-potential solution for vehicular networks, even for distant highway
environments.When collaborating with WiFi/WPANs, the WMAN may provide
the permanent connectivity. The PAN/WiFi portion could be added from the base
stations to the vehicles, as well as among vehicles themselves
(V2V or Vehicle-To-Vehicle multihop communications) to offer high bandwidth with low
cost. Also, a potential protocol could support the direct connectivity of a
vehicle with the WMAN, either when there is lack of a WLAN base station in that
area, or when the number of hops to the base station exceeds some threshold. An
alternative could also be to maintain permanent direct links from vehicles to
cellular base stations, without the direct communication among vehicles.
However, from the cellular network perspective, this will probably result in a
relatively low throughput. Currently, the GSM/GPRS technology ideally offers at
most 100 Kbps of bandwidth. Also 3G systems can reach 384 Kbps. Future cellular
PHY technologies may provide higher throughputs, allowing more data
rate-demanding applications to be supported.

WLANs/WiFiWiFi is another possibility for vehicular networks. An IEEE 802.11 transmitter
has a 250-meter omnidirectional coverage range, which is potentially enough to
maintain a level of multihop connectivity in both highway and urban regions. In
addition, extended-vicinity antennas (umbrellas) could be employed in base
stations, for covering larger distances. A lot of research has been done for
the popular IEEE 802.11 wireless protocol, mostly for the MAC (CSMA/CA) and
network layers. However, this research cannot be taken “off the shelf” for
use in vehicular networks. This is because of the unique properties that we
described above.

WPANsWireless personal area networks are used for short-range wireless
communications. Two of the most popular technologies, Bluetooth and ultrawide
band (UWB), belong to this category. While the former offers a low data rate
(up to 10 Mbps for Bluetooth v2.0), the latter promises very high data rates, up to 500 Mbps, over short distances. Even though there has been a lot of work
done for the PHY layer or UWB, concerning modulation and channelization, only a
few studies exist for upper layers. Especially for UWB ad hoc networks, MAC and
network layer protocols are still under consideration. Even though the data
rates offered by UWB are tempting, the short transmission range (maximum 10–20 m) restricts the applicability of this technology to only very dense urban-area vehicular networks.

Millimeter-Wave CommunicationsThe 60 GHz band is located in the millimeter-wave
portion of the RF spectrum. This part of the spectrum, although very promising,
is largely unexplored with only a
few companies producing FCC-approved wireless products. The major advantages of this technology are the following [5]. (1) It can offer extremely high data rates (due to
the huge available bandwidth) comparable to fiber-optics performance, over
hundred of meters. (2) Very low levels of interference, as well as high levels
of security, mainly due to oxygen absorption and narrow antenna beams. (3)
Potentially high level of frequency reuse. Communications over the 60 GHz band
are promising for applications over vehicular networks. However, this
technology is still quite unknown and unexplored, with only a few research
achievements in its background.In summary, we believe that the most appropriate
wireless technology for vehicular networks, to-date, is the WMAN technology alone, or WMAN in cooperation with WiFi and sometimes with WPANs. The high mobility as well as
the network partitioning and scalability demand the employment of either
infrastructure-based wireless infrastructures or scalable ad hoc solutions,
such as hierarchical clustering structures, and so on. Note also that the millimeter-wave
technology can be largely applicable as long as significant research efforts
are expended towards this solution.

2.3. Applications

Applications
running on top of vehicular networks can be categorized as safety and nonsafety
applications: driver-vehicle safety, infotainment, and mobile internet services
for passengers. In addition to low cost and robust wireless communication
devices, vehicles can also be equipped with storage, processing, and sensing
equipment. Vehicles can be used as
store-and-forward mobile routers, on-demand and dynamic grid computing engines,
as well as distributed mobile sensor networks.

There are a number of projects, completed or under
development, targeting to improve roadway conditions. The Fleetnet project [3], funded by the German Federal Ministry for Education
and Research, focuses on mobile ad hoc radio networks. Fleenet applications
include emergency braking notification and traffic data distribution. The VMesh/VGrid
project [6] has two
directions. In VMesh, vehicles dynamically form a mobile transit network
to gather and dessiminate information. For example, data may be relayed between
different clusters of static nodes that are otherwise disconnected. VGrid targets to evolve intelligent transportation system (ITS) from a
centralized to a distributed approach, in which vehicles can cooperatively
solve traffic-flow control problems. Furthermore, one of the most intelligent
transportation systems exists in Singapore. It includes real-time surveillance
of road speeds, road pricing, advanced traffic signal control, and an advanced
mass transit system. Near-future “smart” vehicular computational devices of various types will be able to communicate with each other and utilize their diverse resources: wireless networks, embedded processors and sensors,
databases, satellites, and so on. This implies the need for innovative
communication protocols, specialized to adopt the unique properties of
vehicular networks and the availability of their resources.

An interesting information architecture toolkit is
discussed in [7]. It
includes (1) wireless networking capabilities, (2) traffic prediction
algorithms, (3) vehicle and trajectory recognition based on fusing
heterogeneous data, (4) cost models (fairness, robustness, privacy,
computational efficiency), and (5) real-time maintenance, prediction and
generation of spatiotemporal information. The kit can be used in a variety of
applications: planning multimodal routes, exchange of real-time traffic
information, autonomous unmanned vehicle driving and, of course, multivehicle
cooperation (MVEC). For this latter application, vehicles are assumed to be
equipped with GPS receivers, computational devices, and wireless communication
systems. Vehicles will be able to process queries, such as “what is the
average vehicle speed 2 miles ahead?”. Processing such queries demands
multihop links and mobile-database utilization.

3. Related Studies on Vehicular Routing

There has been
a lot of interest to exploit the potentials of vehicular networks. However,
only a few studies propose complete routing solutions and architectures. In
this section, we present the most important of these studies. We may have
various categorizations for them. One could be to separate them according to
the type of architecture that they import: ad hoc or infrastructure or hybrid.
Another categorization could involve the deployment region: highway or urban
regions. Below we describe and discuss the most important ones.

Most studies in vehicular routing focus either on
comparing current routing solutions for traditional wireless networks, or
describing issues that must be taken into account, when building appropriate
models. In [8] Füßler et al. examine the applicability of existing ad hoc routing protocols to VANETs. Specifically, they compare the famous Dynamic Source Routing (DSR) and the Greedy Perimeter Stateless Routing (GPSR) protocols. They conclude that
when communication sessions are comprised or more than 2 or 3 hops,
position-based ad hoc routing is preferable over reactive nonposition-based
approaches. The advantages have to do with both the successfully delivered
packets and the control overhead. In addition, the authors argue that the
random waypoint model is rather inappropriate to accurately reproduce vehicle
movement. Alternatively, they make use of the well-validated FARSI simulator,
adopted by many car companies to generate traffic simulation scenarios. For
their simulations, they assume the deployment of the IEEE 802.11 protocol. They
show that current position-based schemes provide high data rates, even over
many hops. Moreover, the overhead is small and does not impact on scalability.
The reason is that position-based routing does not store routes and instead
performs forwarding on the fly. An improvement to DSR could involve
the movement of individual
vehicles in the routing decision. Thus, preference to routes over vehicles
moving in the same direction can be given. As a result, topological changes
would be infrequent. Finally, for position-based schemes, they generally
propose caching and prediction of a node‘s location, based on its speed and direction.

MDDVThe mobility-centric data dissemination algorithm (MDDV) [9] is one of the few that
provide a complete architecture for vehicular routing. It combines the ideas of
opportunistic forwarding, trajectory-based forwarding, and geographical
forwarding. The protocol disseminates data to intended receivers, while
maintaining some design demands (e.g., high-delivery ratio, low delay, and
low-memory occupancy). Even though MDDV can be applied to hybrid architectures,
it is considered in VANET scenarios only.A forwarding trajectory is a predefined path,
extending from the source to the destination region. Moreover, the road network
can be thought of as a directed graph, with nodes representing intersections
and edges being the road segments. One approach would consider taking the
shortest (road) graph distance from the source to the destination region.
However, this does not imply the lowest delay, since node density often leads
to fast propagation. Thus, the authors define(1) as the
dissemination length of the road segment from node A to node B, considering
static road information;(2) as the road length
from A to B. Intuitively, when and , we have (3) as the number of lanes from A/B to B/A. For the
dissemination length, the following formula is used: The constants and take values between and . Constant is set to 5.

The dissemination length is used as weight for the
corresponding link in the graph. The dissemination process has two phases: the
forwarding phase and the propagation phase, described below. Because no
end-to-end connectivity is assumed, messages are forwarded along the forwarding
trajectory through intermediate nodes; these store and forward messages
opportunistically. The vehicle that holds the message and is the closest one to
the destination region is called the message head. To increase
reliability, MDDV allows a set of nodes near the message head to actively
forward the message, instead of the message head alone. However, this also
implies overhead increment. The design issues include the forwarding group
identification, the data exchange procedure, and the decision to store/drop
messages. Each node decides whether it will participate in forwarding or not,
based on the traffic information in the area, as well as with some approximate knowledge
of the message head location. The message head location never moves backward; a new message head location is closer to the destination than previous ones. In a
nut shell, the data exchange steps are the following.

(a) Forwarding PhaseThe message to be sent is assigned an owner. Usually
the owner is the same as the head. Only the message owner may transmit the
certain message, and the owner can be in either one of two states: active or
passive. In the active state, it runs the full protocol to actively propagate
the message. In the passive state, it only transmits the message if it hears an
older version of it.

(b) Propagation PhaseIt is initiated once the message reaches the
destination region. The message now further propagates to each vehicle in the
area centered at the destination, before the message time expires. In this
phase, the message owner can either be in the active state or not transmitting
at all. During this phase, the message is delivered to its recipient(s).

The paper provides a more detailed explanation of the
algorithm, which we avoid reproducing here. It also presents the scheme in only
one forwarding trajectory. However, many of them could be defined to increase
robustness. Also, simulation results show the improved efficiency with regards
to two simple schemes.

GSRIn [10] the Geographic
Source Routing protocol is proposed. The work in [10] examines the problems
appearing with baseline position-based routing in two-dimensional urban
scenarios. GSR combines position-based routing with topological information.
The adoption of the RLS [11] system is assumed. The source uses flooding to
request the position of a node identifier. As soon as that node receives the
request, it sends a position response back to the source. After discovering the
location of the recipient, the source uses a digital map of the roads to
calculate the set of junctions that the packet will follow. This set can be
either imported to the packet header, or be derived by every forwarding node.
This latter approach can be implemented on the basis of greedy forwarding. The
paper discovers the source-destination route through the Dijkstra algorithm. An
issue arises from the fact that the paper compares GSR with nonposition-based
protocols only. There is no comparison with other position-based schemes, such
as GPSR, proposed 3 years earlier.

A-STARThe
authors in [12]
present A-STAR, an anchor-based street and traffic aware routing scheme.
They use information on city bus routes to identify an anchor path with high
connectivity for packet delivery. The model is designed based on position-based
routing, specifically to facilitate VANETs in urban areas. In such
environments, vehicle density is larger in some famous (for their
traffic) roads than in others. Connectivity in such roads can be higher and
more stable due to regular bus passes. Also buildings constrain the signal
propagation. Hence, it is more difficult to establish wireless connectivity in
urban areas; the network efficiency is decreased.A-STAR constructs a graph, based on how many bus lines
go through certain roads. The number of lines determines the link weight for
the certain edge of the graph. The more the routes, the less the weight. Since
each vehicle may be aware of the bus route information through digital maps, an anchor route may be constructed using Dijkstra‘s algorithm for the least weight. Maps with preconfigured routes are called statically rated maps. In
contrast, a dynamically rated one can be utilized. In such a digital map,
weight assignment is performed dynamically by periodically monitoring the
street traffic and updating the graph weights. Message propagation from the source
to the destination follows the route produced by Dijkstra‘s algorithm.The protocol includes its own local route recovery.
The local recovery mechanisms adopted by other protocols, have been proven to
be inefficient in urban areas because of the greedy-forwarding phase. To solve
this problem, A-STAR discovers new anchor paths from the local maximum to which the packet is routed. To prevent other packets from traversing through
the same region, local-maximum streets are marked as OFF. This route
information is disseminated in the network, so as for these routes not to be
used for anchor discovery. The protocol is simulated extensively in [12], compared to GSR and GPSR.
It shows obvious network performance improvement.

P2PA peer-to-peer approach for the support of traffic safety applications is
presented in [13]. The
vehicles (and potential road side access points
communicate via an ad hoc peer-to-peer mechanism. The exchanged data is assumed to be describing vehicular motion, road properties, and warnings or
infotainment data to facilitate traffic safety. However, the scheme can also be
applicable for other types of applications. Moreover, even though the paper
assumes the existence of roadside servers or relays, all the network equipment
is considered as part of the same vehicular network. Network nodes are called
vehicular peers and they utilize ad hoc connectivity. They are organized in
zones, called peer spaces, according to their common interests. Each peer in a
peer space maintains information about all the other peers within the same peer
space. Because the authors focus explicitly on traffic safety, they organize
the peer spaces based on three issues: the communication region, the peer space
composition, and specific parameters of the driving situation. They argue that
there is no advantage for a peer to maintain knowledge for many others. Thus,
each peer space includes at most a number of nodes; they set this to 15 peers.
By this way, information overflow and high overhead are avoided. Peer space
organization can be either cluster-based or peer-centered.

Vehicles decide that their safety will benefit from
associating with neighbors and thus form the peer spaces. An example is
depicted in Figure 1. Vehicle exchanges information with vehicles and and realizes that this data is valuable; hence
vehicle A joins their network. In contrast, node considers this data useless, so it stays out
of the peer space comprised by and . When a node
leaves the cluster, all remaining nodes delete all data for it. Also if a peer
does not receive information about clusters in the area, it will initiate its
own peer space.

Figure 1: Cluster-based organization.

In the peer-centered organization, each vehicle
creates its own peer space. It analyzes data from other participants and
decides which neighbor should be included in its dynamic peer space. Different
peer spaces can be overlapped, as shown in Figure 2.

Figure 2: Peer-centered organization.

The major difference between the approaches is that
the peer-center assumes a peer as the core of a group. The network is organized
according to individual preferences. As a result, such an approach is more
appropriate for urban areas. In contrast, cluster-based is preferable for
highway environments.

The architecture incorporates two kinds of routing:
interspace (between the peer spaces) and intraspace (within a peer space).
Interspace routing is associated with traffic safety, from the perspective of
accident notification to many vehicles on the road. For intraspace routing, the
authors propose mediation mechanisms. All peers include in their packets the
identities of the other peers that are known by senders to be registered in the
same peer space. This information is stored by nodes that receive it. Highly
inefficient flooding can be thus avoided.

The mediation mechanisms employed differ for the
cluster-based and peer-center approaches. For the former, they can be automatic or on demand. In automatic mediation, an individual peer is able to process and analyze data that are transmitted towards another peer in the network. It
can thus determine when a peer has no data for another peer in the space. In
such a case, it will retransmit the missing data. In the on-demand case, peers
that are missing data for others transmit certain messages requesting the
missing data. For the peer-centered organization,
mediation can be automatic only.

Other SchemesSo far we discussed in some detail the most relevant related studies on
vehicular network routing. Here we mention some additional work.In [14] Saha and Johnson present a realistic model for
vehicular motion, which they integrate in the famous -2 simulator, and
argue that their model is more accurate than the random way-point model in some
cases of vehicular movement. The region map is represented as a graph in which
vertices are the road intersections and edges are the road segments. Each node
starts at a random point and moves towards another random point located on a
random destination node. Dijkstra‘s algorithm is used to calculate the route
and movement of the vehicle is constrained along this path.Chisalita and Shahmehri in [15] propose a distributed
protocol for decentralized network organization. The protocol requires the
receivers to analyze the exchanged messages so as to figure out if they are the
intended destinations. For this filtering, the current traffic conditions are
taken into account. The protocol includes mechanisms for message
acceptance/denial, local maintenance of neighborhood information, and
transmission of basic safety (as well as nonsafety) messages.In [16], Namboodiri et al. study the feasibility of mobile
gateways in vehicular ad hoc networks through simulations. They use a simple
mobility model, and various aspects of connectivity along with routing
performance are evaluated. Simulation suggests that each vehicle should be able
to connect to at least one gateway most of the time. The authors evaluate the
effectiveness of the AODV routing protocol and conclude that it performs well,
however they observe frequent link failures. To resolve this, they propose two
simple prediction-based routing protocols to reduce those failures.In [17] Yanlin et al. associate each vehicle with a sector, a
closed area managed by several road-side units. They propose a single-hop agent
advertisement and a single-phase routing scheme, which provides high packet
delivery rates and low overhead.Furthermore, Ding et al. in [18] propose SADV (static-node assisted
adaptive data dissemination), a protocol for assisting in data relaying,
through the use of static nodes at road intersections. With SADV, packets are
temporarily buffered in static nodes until there are vehicles within the
communication range along the best delivery path to further forward the
packets.The discovery of Internet gateways by vehicles is
investigated in [19].
Stationary Internet gateways are assumed at the roadside. Bechler et al. prove
that current routing approaches and classic discovery protocols cannot address
this requirement. They further propose DRIVE, a mechanism that
efficiently discovers Internet gateways. This service discovery protocol
employs an automated method for selecting the most suitable gateway among the
available ones. It uses a fuzzy approach that considers the network properties
and application classes. The location-based service discovery is also examined
in [20]. Klimin et al.
propose a mechanism based on geocast addressing of control messages. This
hybrid approach combines request propagation reactively with a proactive method
for service advertisements. The advantage from this combination is twofold.
First, clients are able to initiate discovery, even when they are located
outside the proactive zone of a service provider. Second, intermediate nodes
may reply to service requests on the border of the provider‘s proactive zone.
This helps saving bandwidth and accelerates the discovery procedure.

4. Security Issues

Generally,
attacks cause anomalies to the network functionality. A lot of previous studies
have investigated security vulnerabilities of routing protocols for wireless
networks. These studies discuss the steps that certain attacks follow to harm the
network. Such attacks can take advantage of algorithmic properties of the
routing protocols. Also, there are attacks in which malicious nodes advertise
fake locations to their neighbor nodes.

As for the first category, routing protocol designers
need to incorporate security measures into the protocols. A designer needs to
consider every aspect of his/her algorithm that could be utilized by malicious
nodes. The network characteristics must also be taken into account. For
example, situations in which the network topology changes dynamically are
tempting to attackers for various reasons. First of all, mobility allows a
modification of the routing table of the victim node simply by moving into the
coverage range of it. The attacker may move away once it succeeds and without
being traced. Moreover, the mobility of legitimate nodes may help attackers
disperse their malicious information (epidemic spreading). Furthermore, the set
of devices within the transmission range of a node keeps changing dynamically.
Besides the algorithmic vulnerabilities, malicious attackers may damage the
network by announcing fake node locations. Such attacks are even more difficult
to mitigate.

The Case of Vehicular NetworksThe unique properties of
vehicular networks that we discussed earlier have an impact on attack
effectiveness. First of all, attacks that target in exhausting the node battery
are not applicable here. Vehicles have the ability of constantly charging their
batteries. Moreover, the vehicle‘s power supply is more than enough to support
energy-demanding computational systems. As a result, authentication processes
do not have to be light-weight.

However, vehicular networks could suffer from other
types of attacks. Specifically, in [4] Dousse et al. prove that the probability of end-to-end
connectivity decreases with distance, for one-dimensional network topologies.
This implies that it now becomes much easier for a malicious attacker to
partition the network. This effect can potentially be addressed by maintaining
multiple forwarding nodes for each packet. For example, in MDDV the protocol
allows a group of vehicles near the message head to actively propagate
the message. Hence, if we only have one or a few malicious nodes, the rest of
them could potentially maintain the node reliability. However, a synchronized
attack by multiple compromised vehicles would be disastrous. More than that,
vehicular networks are expected to show large scalability. This, together with
the unreliability of single vehicles, is ideal for applying even simple
attacks.

On the other hand, even though vehicular movement can be quite fast,
it is rather predictable. This does not mean that we can always know the exact
direction of a vehicle; however, a probabilistic or stochastic approximation
could be incorporated in previous authentication studies. Especially for
location verification methods, the predicted mobility of the claimant could be easily employed from the verification algorithm. As a result, location
estimation methods designed for traditional wireless networks can be adopted
for vehicular networks, with minor modifications. No modifications may be
required for some of them. This is because those mechanisms rely on signals
transmitted either with the speed of light or with the speed of sound, or a
combination of them. It is rather impossible that the average vehicle speeds
will reach the speed of sound, at least in the near future, even for highway
environments. Hence, the relative (to the mechanisms) vehicular speeds are not
expected to affect the validity of these mechanisms. Our prediction could be
supported by [9]; Wu
et al. argue that the traffic in the opposite direction of the desired
information flow is less helpful than the traffic in the same direction. Since
the relative speeds in the same direction can be considered negligible, they
will not affect the verification methods. This is because verifiers and
claimant are expected to have the same approximate speed. In case the claimant
travels in the opposite direction, it is less likely that it will be part of a
network in its opposite direction. If this is the case however, it can be more
difficult for verifiers to correctly estimate its actual location.

In [21] Zarki et al. discuss some general security and
privacy issues. They present DAHNI, a simple vehicular communication
infrastructure, without deeply analyzing its details. The article assumes no
confidentiality for the transmitted data and that data is highly
delay-sensitive. It is argued that

(1)no key
distribution is usually required (no bulk data is transmitted and vehicles are
not likely to stay in a cell for a long time);(2)explicit
handoffs are not required if communication is largely one-way (i.e., vehicles
reporting their properties to the base station). A potential
simple security architecture for vehicular networks should at least include (1)
digital signatures, (2) time-stamping and sequencing, and (3) a certification
infrastructure. Even though the authors do not proceed to some kind of
implementation/simulation, their scheme seems reasonable, while the required
technology components are available nowadays.

5. Conclusion

In this paper,
we investigated routing aspects of vehicular networks. We identified the
potential wireless technologies, properties, architectures, security concerns,
and previous studies. After presenting the proposed models, we commented on
their efficiency and feasibility.

Vehicular networks are expected to be very attractive
in the near future, facilitating numerous applications. However, to fully
exploit their advantages, network designers need to take into account the
unique characteristics of such networks.

Acknowledgments

The authors would like to
thank the editor Jianping Pan as well as the anonymous reviewers for their
constructive comments on their work.

I. Chisalita and N. Shahmehri, “A peer-to-peer approach to vehicular communication for the support of traffic safety applications,” in Proceedings of the 5th IEEE International Conference on Intelligent Transportation Systems, pp. 336–341, Singapore, September 2002.View at Publisher · View at Google Scholar